Filling in data gaps with satellites makes the last few years a little warmer.

If you want to take someone’s temperature to see if they have a fever, you know where to put the thermometer. (Sorry, infants.) But where do you take the temperature of Earth’s climate? Inconveniently, the answer is “everywhere”—you need measurements covering the planet to properly calculate the global average surface temperature. That’s no big deal for Europe, where a local weather station is never far away, but it's much more of a problem for the North and South Poles where records are hard-won. A new analysis shows that how you deal with this problem makes a difference in what temperature you end up reading.

Building a global temperature dataset is a huge undertaking, because that’s only the half of it. Lots of careful corrections need to be made to the raw measurements to account for things like instrument changes, weather station placement, and even the time of day the station is checked.

One of the most commonly used datasets, dubbed “HadCRUT4” in its current incarnation, is maintained by the UK Met Office and researchers at the University of East Anglia. That dataset lacks temperature records over 16 percent of the globe, mostly parts of the Arctic, Antarctic, and Africa. Each group that manages one of these datasets faces this problem but deals with it a little differently. In HadCRUT4, the gaps are simply dropped out of the calculated average; in NASA’s GISTEMP dataset, these holes are filled in by interpolating from the nearest measurements.

The new study by Kevin Cowtan of the University of York and Robert Way of the University of Ottawa evaluates a few different methods for dealing with these gaps and shows that HadCRUT4 has probably been off by an important amount over the last few years.

Cowtan and Way did what the people behind the big datasets do: left out the holes or filled them in using the nearest data. But they also developed a different technique that took advantage of satellite data. Satellites may seem like the obvious solution to the problem of maintaining thermometers in the middle of Antarctica, but they have their own problems. First, it’s tricky to measure the temperature of a specific, and very thin, layer of the atmosphere from orbit, since your instrument has to look through all of the rest of the atmosphere to do so. Second, the layer the satellites are monitoring extends much higher above the surface than our thermometers do.

Instead of merely copying satellite data and pasting it into the surface thermometer gaps, they used the satellite data to intelligently guide the filling of the holes with the closest surface measurements.

To test these three methods, the researchers artificially removed measurements across regions of the Earth and compared how close each method came to correctly calculating the global mean of the complete dataset. Just filling in with the nearest data (the NASA method) did a little better than leaving the gaps out (the HadCRUT4 method), but the method that used satellite data performed best.

Applying that method to the actual holes in the HadCRUT4 dataset yielded some interesting results, seen in the image below. Most notably, the abnormally warm El Niño year of 1998 becomes a little less extreme, and most of the last decade gets a little warmer. The difference in the last few years comes from the missing data in the Arctic, which is the fastest-warming region on Earth.

Together, those small changes (which are within the HadCRUT4 error bars) have a big impact on the calculated short-term warming trend. The trend for the period from around 1998 to present, which opponents of action on climate change frequently point to when arguing that climate scientists have got it all wrong, more than doubles to 0.12 °C per decade.

Enlarge/ Thin lines show the HadCRUT4 average global surface temperature compared to the newly calculated global average using satellite data to help fill in gaps (thick lines). Red lines show trends for the period 1997-2012.

Scientists know better than to read too much into short-term trends because of the natural, year-to-year variability of Earth’s climate (caused by things like volcanoes or El Niño conditions in the Pacific) and because of the error bars on global temperature datasets. We know, for example, that a cluster of La Niñas in the last decade have had a cooling influence on surface temperatures, while the deep ocean appears to have been taking up more than its usual share of the warming. Add in a little error in the global average temperature, and the real story starts to look a lot different than some had assumed.

In any case, small wiggles can have a lot of leverage on a short-term trend. This is why climate conditions are typically characterized by periods of 30 years or more.

The goal of this study was to find out how important the missing data (including parts of the rapidly changing Arctic) were to global temperature datasets. The results show that they can have a pretty big influence, emphasizing the need to keep our eyes on the Arctic.